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<p>Species delimitation methods based on macromorphology are often limited by phenotypic plasticity in plants. Fourier Transform near-infrared spectroscopy (FT-NIR) provides a promising alternative as a non-destructive technique that measures molecular vibrations (overtone and combination bands of C–H, N–H, and O–H bonds) from plant tissue exposed to near-infrared light (780–2,500&#xa0;nm). We applied FT-NIR to the taxonomically challenging Scaly clade of <i>Microgramma</i> ferns (94 samples, eight species), including dimorphic and monomorphic taxa, to evaluate its diagnostic potential. Using multivariate models and cross-validation, we achieved 81–100% average identification accuracy. Well-defined species (e.g., <i>M. percussa</i>) reached 100% accuracy, while morphologically overlapping taxa showed lower accuracy, likely due to hybridization, introgression, or cryptic variation. Dimorphic species exhibited higher intraspecific spectral variability and lower accuracy linked to differences between fertile/sterile fronds than monomorphic species. FT-NIR proves effective as a complementary tool for fern systematics, elucidating species limits and diversity patterns. Further studies should address how hybridization, introgression, and indumentum affect spectral data.</p>

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A comparison of interspecific and intraspecific phenotypic variation in spectral signatures of ferns with robust versus uncertain species boundaries

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Niksoney Azevedo Mendonça,
  • Marise Helen Vale de Oliveira,
  • Thaís Elias Almeida

摘要

Species delimitation methods based on macromorphology are often limited by phenotypic plasticity in plants. Fourier Transform near-infrared spectroscopy (FT-NIR) provides a promising alternative as a non-destructive technique that measures molecular vibrations (overtone and combination bands of C–H, N–H, and O–H bonds) from plant tissue exposed to near-infrared light (780–2,500 nm). We applied FT-NIR to the taxonomically challenging Scaly clade of Microgramma ferns (94 samples, eight species), including dimorphic and monomorphic taxa, to evaluate its diagnostic potential. Using multivariate models and cross-validation, we achieved 81–100% average identification accuracy. Well-defined species (e.g., M. percussa) reached 100% accuracy, while morphologically overlapping taxa showed lower accuracy, likely due to hybridization, introgression, or cryptic variation. Dimorphic species exhibited higher intraspecific spectral variability and lower accuracy linked to differences between fertile/sterile fronds than monomorphic species. FT-NIR proves effective as a complementary tool for fern systematics, elucidating species limits and diversity patterns. Further studies should address how hybridization, introgression, and indumentum affect spectral data.